The influence of network structural preference on link prediction
type: Item
instance of: scholarly article
title: The influence of network structural preference on link prediction
zbMATH Open document ID: 1459.91152
DOI: 10.1155/2020/6148273
author: Yu Wang
author: Xinye Lin
author: Wei Wang
author: Yongcheng Wang
published in: Discrete Dynamics in Nature and Society
publication date: 2020-10-14T00:00:00Z
review text: Summary: Link prediction in complex networks predicts the possibility of link generation between two nodes that have not been linked yet in the network, based on known network structure and attributes. It can be applied in various fields, such as friend recommendation in social networks and prediction of protein-protein interaction in biology. However, in the social network, link prediction may raise concerns about privacy and security, because, through link prediction algorithms, criminals can predict the friends of an account user and may even further discover private information such as the address and bank accounts. Therefore, it is urgent to develop a strategy to prevent being identified by link prediction algorithms and protect privacy, utilizing perturbation on network structure at a low cost, including changing and adding edges. This article mainly focuses on the influence of network structural preference perturbation through deletion on link prediction. According to a large number of experiments on the various real networks, edges between large-small degree nodes and medium-medium degree nodes have the most significant impact on the quality of link prediction.
Mathematics Subject Classification ID: 91D30
Mathematics Subject Classification ID: 05C82
zbMATH DE Number: 7260923
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2020/6148273
OpenAlex ID: W3087843486
cites work: Statistical mechanics of complex networks
cites work: Networks
cites work: Toward link predictability of complex networks
cites work: Collective dynamics of ‘small-world’ networks
description: scientific article; zbMATH DE number 7260923